import numpy as np
import matplotlib


def colorize_depth(depth: np.ndarray, mask: np.ndarray = None, normalize: bool = True, cmap: str = 'Spectral') -> np.ndarray:
    if mask is None:
        depth = np.where(depth > 0, depth, np.nan)
    else:
        depth = np.where((depth > 0) & mask, depth, np.nan)
    disp = 1 / depth
    if normalize:
        min_disp, max_disp = np.nanquantile(disp, 0.001), np.nanquantile(disp, 0.999)
        disp = (disp - min_disp) / (max_disp - min_disp)
    #colored = np.nan_to_num(matplotlib.colormaps[cmap](1.0 - disp), 0)
    #colored = (colored.clip(0, 1) * 255).astype(np.uint8)[:, :, :3]
    grayscale = np.nan_to_num(disp, nan=0.0)
    grayscale = (grayscale * 255).astype(np.uint8)
    return grayscale


def colorize_depth_affine(depth: np.ndarray, mask: np.ndarray = None, cmap: str = 'Spectral') -> np.ndarray:
    if mask is not None:
        depth = np.where(mask, depth, np.nan)

    min_depth, max_depth = np.nanquantile(depth, 0.001), np.nanquantile(depth, 0.999)
    depth = (depth - min_depth) / (max_depth - min_depth)
    colored = np.nan_to_num(matplotlib.colormaps[cmap](depth), 0)
    colored = (colored.clip(0, 1) * 255).astype(np.uint8)[:, :, :3]
    return colored


def colorize_disparity(disparity: np.ndarray, mask: np.ndarray = None, normalize: bool = True, cmap: str = 'Spectral') -> np.ndarray:
    if mask is not None:
        disparity = np.where(mask, disparity, np.nan)
    
    if normalize:
        min_disp, max_disp = np.nanquantile(disparity, 0.001), np.nanquantile(disparity, 0.999)
        disparity = (disparity - min_disp) / (max_disp - min_disp)
    colored = np.nan_to_num(matplotlib.colormaps[cmap](1.0 - disparity), 0)
    colored = (colored.clip(0, 1) * 255).astype(np.uint8)[:, :, :3]
    return colored


def colorize_segmentation(segmentation: np.ndarray, cmap: str = 'Set1') -> np.ndarray:
    colored = matplotlib.colormaps[cmap]((segmentation % 20) / 20)
    colored = (colored.clip(0, 1) * 255).astype(np.uint8)[:, :, :3]
    return colored


def colorize_normal(normal: np.ndarray) -> np.ndarray:
    normal = normal * [0.5, -0.5, -0.5] + 0.5
    normal = (normal.clip(0, 1) * 255).astype(np.uint8)
    return normal